Measuring overuse with electronic health records data.
Bibliographic record
Abstract
OBJECTIVES: To measure overuse of low-value care using electronic health record (EHR) data and manual chart review and to evaluate whether certain low-value services are better captured using EHR data. STUDY DESIGN: We implemented algorithms to extract performance on 13 Choosing Wisely-identified healthcare services using EHR data at a large physician practice group between 2011 and 2013. METHODS: We calculated rates of overuse using automated EHR extracts. We manually reviewed the charts for 200 cases of overuse for each measure to determine if they had clinical risk factors that could explain use of the low-value service and then calculated adjusted rates of overuse. We explored trends in overuse for each low-value service in the 3-year duration using logistic regression. RESULTS: Unadjusted rates of overuse ranged from 0.2% to 92%. Automated EHR extracts and manual chart review identified explanatory risk factors for most measures, although the magnitude varied: for some measures (eg, bone densitometry exam for women younger than 65 years), manual chart review did not identify many additional risks (3.0%). In contrast, in patients who had sinus computed tomography or an antibiotic prescription for uncomplicated acute rhinosinusitis, manual chart review identified more explanatory risk factors (22.5%) than the automated EHR extract (9.5%). Adjusted rates of overuse ranged from 0.2% to 61.9%. Eight services demonstrated a statistically significant decrease in overuse over 3 years, while 1 increased significantly. CONCLUSIONS: The use of EHR data, both extracted and manually abstracted, provides an opportunity to more accurately and reliably identify overuse of low-value healthcare services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".